Map

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Map

We define a local authority to be a hotspot if weekly cases per 100,000 population exceed 50. For past weeks we compare the observed cases to the threshold. For future weeks, we give probabilities based on our model, which assumes a situation in which no additional interventions (e.g. local lockdowns) occur. We consider a local authority to have increasing infections (likely increasing infections) if our model estimates that with probability of at least 90% (75%) the reproduction number Rt is greater than 1 . Decreasing and likely decreasing are defined analogously, but consider Rt less than 1 .

Table

Median and 90% credible interval for weekly cases and Rt can be viewed via the “Column Visibility” button. Colour code for cases in table: 0.00 – 0.25 0.25 – 0.50 0.50 – 0.70 0.70 – 0.90 0.90 – 0.95 0.95 – 1.00. Colour code for Rt in table: 0.00 – 0.1 0.1 – 0.25 0.25 – 0.75 0.75 – 0.90 0.90 – 1.00.

Details

Local authority search

Model

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Model Description

The results on this page have been computed using epidemia 0.6.0. Epidemia extends the Bayesian semi-mechanistic model proposed in the Flaxman, S., Mishra, S., Gandy, A. et al. Nature 2020.

The model is based on a self-renewal equation which uses time-varying reproduction number \(R_{t}\) to calculate the infections. However, due to a lot of uncertainty around reported cases in early part of epidemics, we use observed deaths to back-calculate the infections as a latent variable. Then the model utilizes these latent infections together with probabilistic lags related to SARS-CoV-2 to calibrate against the observed deaths and the reported cases in the last 30 days. A detailed mathematical description of the model can be found here.

\(R_{t}\) for each local authority is parameterized as a linear function of the \(R_t\) for Engalnd and Wales as a whole (which we fit too), and a random effect specific to the local authority for each week over the course of the epidemic. The weekly random effects are encoded as a random walk, where at each successive step the random effect has an equal chance of moving upward or downward.

Limitations

  • Predictions on this page assume no interventions (lockdowns, school closures, and others) in the local area beyond those already taken about a week before the end of observations.
  • An increase in cases in an area can be due to an increase in testing. The model currently does not account for this.
  • Each region (Local authority) is treated independently, i.e., epidemic in a region is neither affected by nor affects any other region.
  • The population within a local authority is considered to be homogeneous, i.e., all individuals are considered equally likely to be affected by the disease progression.

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Data sources

  • Daily cases data for all local authorities in England is taken from UK gov site
  • Daily cases data for all local authorities in Wales is taken from Public Health Wales COVID-19 dashboard
  • Weekly deaths data for all Local authorities in England and Wales comes from ONS

Imprint

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Imprint

Publisher

Authors: Swapnil Mishra1, Jamie Scott2, Harrison Zhu2, Neil Ferguson1, Samir Bhatt1, Seth Flaxman2, Axel Gandy2

1MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London

2Department of Mathematics, Imperial College London

Website Development: Aided by Fabian Valka

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Privacy Policy

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Contact

Contact

For any enquiries please contact:

Corresponding Author
Axel Gandy
a.gandy@imperial.ac.uk

External Relationships and Communications Manager
Sabine L. van Elsland
+44 (0)20 7594 3896

mrc.gida@imperial.ac.uk